Statistical Modeling of Infarct Growth in Acute Ischemic Stroke

Perfusion imaging with a CT scanner provides important information about blood flow and, in acute ischemic stroke, highlights regions of brain infarct, where tissue has died because of a lack of blood flow. This infarct region continues to grow over time until blood flow is restored. Being able to predict the rate of infarct growth would provide doctors with a key piece of information (currently unavailable) that would help them decide on a course of treatment. The project proposed here will use advanced statistical methods to both determine and characterize the factors that affect infarct growth and model the relationship so that it can be applied prospectively in acute ischemic stroke cases. QuikFlo Technologies will be able to incorporate Dr. Pordelli’s results into their automated stroke triaging support tool that will provide doctors with the information needed to make the best possible choice of treatment for individual stroke patients.

Faculty Supervisor:

Michael Hill


Pooneh Pordeli


University of Calgary




Life sciences


University of Calgary



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